Digital imaging has become increasingly popular, especially in electronic commerce which is increasingly being used by sellers to conduct business and sell items to customers. Customers are able to efficiently view and purchase a wide variety of items, including both goods and services, over computer networks, including the Internet. The same goods and services can be offered by multiple sellers, each with its own description of the item for sale (i.e., including an image), allowing a customer to quickly and easily select any desired item from any given seller by using each seller's image.
The amount of image files being used for such purposes is increasing dramatically. But with the increasing number of image files comes an increasing number of duplicate images, as well as minor variations between different image files meant to identify the same item. Indeed, the minor variations between image files can create apparent duplication of subject matter that is difficult to distinguish when a customer is looking for something specific. In certain instances, the different images (or image sets) for the same item can even be provided by the same source, increasing confusion by the consumer. To avoid that confusion, one option has been to present all images meeting the criteria used to identify the item. This option is distracting and time consuming because the customer must look at every single image in the purchasing process.
Thus, from a customer's perspective, it would be helpful if only a single image (or image set) is displayed since this would simplify the viewing and purchasing process for the customer. In most instances, that single image (or image set) that is displayed to the customer should accurately depict the item. But often the different images (or image sets) are provided by different sellers, and processed by multiple sellers each with their own conditions and criteria, before being displayed to the customer. The different images, not to mention the different conditions and criteria, can further confuse the customer and complicate the purchasing process.
So there exists a problem of how to select a single image that best depicts the item for sale. Most methods address this problem by manually selecting the best image. But it is not unusual in these methods to be confronted with thousands of images. And requiring someone to manually filter through thousands of images is onerous, not to mention time consuming and expensive.
Some methods address this problem by selecting the images based on non-image based characteristics, such as the date or title accompanying the images. But selecting the images to display to the customer using such methods often does not result in selecting the single image (or image set) that best depicts the item.